AI presents exciting possibilities for both academic and business research, but there are gaps that need addressing. Here’s a breakdown of some key areas:

Academic Research Gaps:

• Social and Technical Divide: There’s a tendency to focus on the technical aspects of AI development, neglecting the social implications. Research is needed to bridge this gap, considering ethical issues, bias in algorithms, and the broader impact of AI on society [1].
• Multidisciplinary Integration: AI research often falls within specific disciplines. More studies are needed that explore the intersection of AI with social sciences, humanities, and other fields to gain a more holistic understanding [1].

Business Research Gaps:

• Practical Applications: While there’s much hype around AI’s potential, research on how businesses can develop and implement AI capabilities for concrete benefits is lacking [2]. This includes cost-effectiveness, talent acquisition, and building internal expertise.
• Specific Applications in Different Fields: More research is needed on how AI can be applied effectively in specific business areas like supply chain management or human resource management [2]. The impact on different management levels also needs exploration.
• AI’s Impact on Human Workers: How AI will change the nature of work and the skills required is a crucial area. Research should examine how AI can complement human strengths and address potential job displacement anxieties.

These are just some examples, and the specific gaps will vary depending on your research field. But AI’s potential is undeniable, and addressing these gaps will be crucial for its successful development and application in both academia and business.

Also, from another source:

Artificial Intelligence (AI) is increasingly being utilized to identify and address research and business gaps in various fields. Here’s how AI can be leveraged for academic and business research:

1. Literature Review Automation: AI can expedite the literature review process by automatically identifying relevant papers, summarizing their key findings, and highlighting gaps in existing research. Tools like semantic search engines, text mining, and natural language processing (NLP) algorithms can help researchers quickly sift through vast amounts of literature to identify knowledge gaps.
2. Data Analysis and Insights: AI algorithms can analyze large datasets to uncover patterns, trends, and insights that may not be immediately apparent to human researchers. Machine learning techniques, such as clustering, classification, and regression, can help identify correlations and causal relationships in data, thereby highlighting research gaps or areas for further investigation.
3. Predictive Analytics: AI-powered predictive analytics can forecast future trends and outcomes based on historical data. By analyzing past research trends and performance metrics, AI can help identify emerging research areas and potential gaps in existing literature.
4. Automated Hypothesis Generation: AI systems can generate hypotheses based on existing knowledge and data, thereby suggesting new research directions or hypotheses that researchers may not have considered. This can help identify gaps in current understanding and guide the formulation of research questions.
5. Text Generation and Summarization: AI can generate coherent summaries of research articles, reports, and other textual sources, making it easier for researchers to quickly grasp the main ideas and identify areas where further investigation is needed.
6. Collaborative Filtering and Recommendation Systems: AI-powered recommendation systems can suggest relevant research papers, collaborators, and funding opportunities based on researchers’ interests, expertise, and past activities, thereby facilitating collaboration and knowledge exchange across different disciplines.
7. Expert System for Decision Support: AI-powered expert systems can provide decision support to researchers and business professionals by synthesizing existing knowledge, identifying gaps, and recommending the most promising research directions or business strategies.
8. Semantic Analysis for Conceptual Mapping: AI techniques such as semantic analysis and knowledge graph construction can help map out the conceptual landscape of a research field, highlighting connections between different concepts and revealing gaps in knowledge or understanding.
9. Natural Language Generation for Research Proposals and Reports: AI can automate the process of writing research proposals, reports, and manuscripts by generating coherent and contextually relevant text. This can save researchers time and effort, allowing them to focus more on critical thinking and analysis.
10. Continuous Monitoring and Feedback Loop: AI systems can continuously monitor research trends, feedback from stakeholders, and changes in the business environment to identify evolving gaps and opportunities, ensuring that research efforts remain relevant and impactful over time.

By leveraging AI technologies, researchers and business professionals can enhance the efficiency, effectiveness, and impact of their research efforts, ultimately leading to the generation of new knowledge and insights that address critical gaps in academia and industry.